Facilities or outlets such as bank branches, retail store, automobile dealer, and like are considered important but costly channels for companies in serving their customers and winning in the competitive marketplace. A critical issue to address is how to evaluate the facility site location and optimize the site network to serve more customers. In those problems, a key point is to construct the location evaluation model to judge how much the location is suitable for opening such a facility. Known methods combine multiple facility performance metrics by weighted sum to setup the location evaluation model. Such models, however, are not completely accurate, and it is difficult to assign the weight coefficients properly.
An accurate location evaluation model is difficult to construct because of the very complex evaluation mechanism and lack of sample data. For instance, a mechanism for evaluating location should consider multiple metrics simultaneously to evaluate whether a location is good or bad. For example, in banking, it should consider several metrics such as deposit, loan, financial service revenue, and cost, however, it is difficult to model those factors into a single objective function to be optimized. Known methods combine multiple facility performance metrics by weighted sum to setup the location evaluation model, however, it is difficult to assign the weight coefficients properly, and the models usually are not accurate. Further, known methods do not allow users to input their knowledge or experience to aid in evaluation.
Lack of rating data of facility locations is another challenge posed in evaluating locations. It is almost impossible to obtain the complete rating data of facility locations, making it difficult to conclude the evaluation rules from sample data using statistical or learning methods.
Thus, what is desirable is a method and apparatus for location evaluation and site selection, capable of effectively configuring the site network and evaluating the facility location by scientifically modeling and incorporating human knowledge.